4c38f4d32829de2abb368bedbef346f888a28c2c,nonconformist/cp.py,TcpClassifier,predict,#TcpClassifier#Any#Any#,107

Before Change

				scores = self.base_icp.nc_function.score(train_x, train_y)
				n_gt = sum(scores > scores[-1])
				n_eq = sum(scores == scores[-1])
				if self.smoothing:
					r = np.random.uniform(0, 1)
					p[i, j] = (n_gt + r*n_eq) / (n_train + 1)
				else:
					p[i, j] = (n_gt + n_eq) / (n_train + 1)

		if significance is not None:
			return p > significance
		else:
			return p

After Change

				train_y = np.hstack([self.train_y, y])
				self.base_icp.fit(train_x, train_y)
				scores = self.base_icp.nc_function.score(train_x, train_y)
				n_gt = (scores[:-1] > scores[-1]).sum()
				n_eq = (scores[:-1] == scores[-1]).sum()

				p[i, j] = calc_p(n_train, n_eq, n_gt, self.smoothing)

Instances